Unsupervised domain adaptation (UDA) refers to the process of bridging this gap to improve the performance of source-trained networks on unlabelled target samples. There have been several recent works addressing this problem, for both 2D [1]}, [2]}, [3]} and 3D [4]}, [5]}, [6]}, [7]}, [8]} object detection. However, source samples are often unavailable during training due to limited memory capabilities or privacy reasons. This requires a source-free domain adaptation approach, where only the source-trained model and unlabelled target samples are used for adaptation. There exist several such approaches for various computer vision tasks on images [9]}, [10]}, [11]}. Only SF-UDA3D [6]} attempts this setting for 3D object detection, but relies on a sequence of lidar frames as an input to the network.
